Data-deficient river and lake water level prediction method based on multi-source remote sensing information and machine learning algorithm

By combining multi-source remote sensing information with machine learning algorithms, the problem of lack of ground monitoring equipment was solved, an intelligent prediction model for river and lake water levels was constructed, the accuracy of river and lake water level predictions was improved, and the prediction needs of river basin flow and water resources were met.

CN118395275BActive Publication Date: 2025-09-16CHINA INST OF WATER RESOURCES & HYDROPOWER RES +1
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Patent Information

Application Number
CN202410112992.0
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-01-26
Publication Date
2025-09-16
Estimated Expiration
2044-01-26

AI Technical Summary

Technical Problem

In existing technologies, the lack of ground monitoring equipment and meteorological and hydrological observation data leads to low accuracy in river and lake water level predictions. Traditional physical mechanism models find it difficult to accurately depict the hydrological cycle, resulting in high uncertainty in river and lake water level prediction results, making it difficult to meet business needs.

Method used

By using multi-source remote sensing information and machine learning algorithms, and acquiring satellite remote sensing products for data fusion and processing, an intelligent prediction model for river and lake water levels is constructed. The machine learning model is used to learn the driving mechanism of meteorological factors and river and lake water levels, reducing errors caused by inaccurate characterization of physical processes.

Benefits of technology

It improves the accuracy of river and lake water level predictions, provides key data support, and meets the business needs of basin flow and water resources prediction.

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Abstract

The present invention discloses a data-deficient river and lake water level prediction method based on multi-source remote sensing information and a machine learning algorithm. The method utilizes multiple satellite altimetry products to obtain historical river and lake water level data, multiple remotely sensed precipitation products to obtain historical precipitation data for river and lake basins, and multiple remotely sensed evapotranspiration products to obtain historical evapotranspiration data for river and lake basins. A machine learning model is introduced, using remotely sensed inverted river and lake water level data, precipitation data, and evapotranspiration data as the primary training data. With water level as the prediction variable and precipitation and evapotranspiration as the primary predictive factors, an intelligent river and lake water level prediction model is established. The model then uses corrected future precipitation forecast data to predict future river and lake water level changes. The advantages of this method are: By using big data methods to learn the driving mechanisms between basin meteorological factors and river and lake water levels, the method reduces the errors and uncertainties caused by the inaccurate depiction of physical processes by traditional mechanism models, thereby further improving the accuracy of river and lake water level predictions.
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